Compared Methods.
We compare GraphCache with the following state-of-the-art RE models:
(1) PA-LSTM [1]} extends the bi-directional LSTM by incorporating positional information to the attention mechanism.
(2) GCN [2]} uses a graph convolutional network to gather relevant contextual information along syntactic dependency paths.
(3) C-GCN [2]} combines GCN and LSTM, leading to improved performance than each method alone.
(4) C-SGC [4]} simplifies GCN by removing the nonlinear layers and achieves higher effectiveness.
(5) SpanBERT [5]} extends BERT by introducing a new pretraining objective of continuous span prediction.
(6) RECENT [6]} restricts the candidate relations based on the entity types.
(7) LUKE [7]} pretrains the language model on both large text corpora and knowledge graphs and further proposes an entity-aware self-attention mechanism.
(8) IRE [8]} proposes an improved entity representation technique in data preprocessing, which enables RoBERTa to achieve state-of-the-art performance on RE.
